In a traditional vehicle insurance claim settlement scenario, an insurance company needs to send professional survey and damage assessment personnel to a scene of an accident to conduct an on-site survey and damage assessment, propose a vehicle's repair plan and compensation amount, take photos of the scene, and record the damage assessment photos for damage and compensation check by off-site inspectors. Due to the need for manual survey and damage assessment, the insurance company needs to invest a lot of labor costs and professional training costs. In terms of the experience of ordinary users, they have to wait for a manual surveyor to take pictures on the spot, a damage assessor to assess damage at a repair location, and a damage inspector to conduct a damage check during the claim settlement process. Therefore, the claim settlement period takes up to 1-3 days, the user waiting time is long, and the user experience is poor.
In view of the above, the inventors apply artificial intelligence and machine learning to the scene of damage assessment for a vehicle, and use the computer vision image identification technology in the field of artificial intelligence to automatically recognize, according to on-site damage pictures taken by ordinary users, the vehicle damage status reflected in the pictures, and automatically provide a repair plan. Therefore, manual surveys, damage assessments, and checks in the traditional vehicle insurance claim settlement scenario are not required, which could greatly reduce insurance company costs and improve the vehicle insurance claim settlement experience of users.
Moreover, the accuracy of damage identification in current intelligent damage assessment solutions needs to be further improved. Therefore, an improved solution is also desired for further optimizing vehicle damage detection results and improving identification accuracy.